Assumption of Responsibility and Loss of Bargain in\nTort Law
Bibliographic record
Abstract
The author seeks to justify recovery in negligence law for loss of bargain, which is the pure economic loss incurred by a subsequent purchaser of a defective product or building structure in seeking to repair the defect. The difficulty is that the purchaser is not in a relationship of contractual privity with the manufacturer The conflicting approaches in Anglo-American tort law reveal confusion, owing to loss of bargain's dual implication of the law governing pure economic loss and products liability. These difficulties are overcome by drawing from Hedley Byrne's requirements of a defendant's assumption of responsibility and a plaintiff's reasonable reliance, and by casting the damaged interest as that of the plaintiff's own autonomy. In doing so, the doctrine of assumption of responsibility is encapsulated, and the case for its extension to loss of bargain cases is made with reference to early U.S. products liabilityjurisprudence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".